llm-mlc VS TypeChat

I'm still iterating on that. Plugins get complete control over the prompts, so they can handle the various weirdnesses of them. Here's some relevant code:

https://github.com/simonw/llm-gpt4all/blob/0046e2bf5d0a9c369...

https://github.com/simonw/llm-mlc/blob/b05eec9ba008e700ecc42...

https://github.com/simonw/llm-llama-cpp/blob/29ee8d239f5cfbf...

I'm not completely happy with this yet. Part of the problem is that different models on the same architecture may have completely different prompting styles.

I expect I'll eventually evolve the plugins to allow them to be configured in an easier and more flexible way. Ideally I'd like you to be able to run new models on existing architectures using an existing plugin.

What is the advantage of this versus running something like https://github.com/simonw/llm , which also gives you options to e.g. use https://github.com/simonw/llm-mlc for accelerated inference?

I'm quite impressed with Llama 2 13B - the more time I spend with it the more I think it might be genuinely useful for more than just playing around with local LLMs.

I'm using the MLC version (since that works with a GPU on my M2 Mac) via my https://github.com/simonw/llm-mlc plugin.

Not sure it's related to function calling. GPT4 can do function calling without using the specific function-calling API just by injecting the schema you want into the prompt with directions and asking it to return JSON. It works like >99% of the time. Same with 3.5-turbo.

The problem is these libraries convert pydantic models into json schemas and inject them into the prompt, which uses up like 80% more tokens than just describing the schema using typescript type syntax for example. See https://microsoft.github.io/TypeChat/, where they prompt using typescript type descriptions to get json data from LLMs. It's similar to what we built but with more boilerplate.

Semantic Memory (renamed to Kernel Memory - https://github.com/microsoft/kernel-memory) complements SK. Guidance's features are being absorbed into SK, following the departure of that team from Microsoft. Additionally, we have TypeChat (https://github.com/microsoft/TypeChat), which aims to ensure type-safe responses from LLMs. Most features of Autogen are also being integrated into SK, along with Assistants. SK serves as the orchestration engine powering Microsoft Copilots.

I tried TypeChat for my use case and ended up defining functions as typescript data types. This approach sounds much better, and leverages the newer OpenAI function calling, which should be more reliable I would think. Thanks for creating+sharing.

https://microsoft.github.io/TypeChat/

That re-prompting error on is what this new Microsoft library does, too: https://github.com/microsoft/TypeChat

Here's their prompt for that: https://github.com/microsoft/TypeChat/blob/c45460f4030938da3...

I think the approach using grammars (seen here, but also in things like https://github.com/ggerganov/llama.cpp/pull/1773 ) is a much more elegant solution.

I'm very surprised that they're not using `guidance` [0] here.

It not only would allow them to suggest that required fields be completed (avoiding the need for validation [1]) and probably save them GPU time in the end.

There must be a reason and I'm dying to know what it is! :)

[0] https://github.com/microsoft/guidance

[1] https://github.com/microsoft/TypeChat/blob/main/src/typechat...